Blood Lead Concentrations and Cardiovascular Mortality in the United States: The NHANES Mortality Follow-up Cohort Study
Bibliographic record
Abstract
Background: Environmental lead exposure, measured using blood lead concentration, is an established risk factor for hypertension, but the relation of blood lead concentration with cardiovascular disease (CVD) mortality and the number of deaths attributable to it are uncertain. Aims: To quantify the contributions of lead exposure to all-cause mortality and CVD mortality in the United States. Methods: We quantified the contribution of blood lead concentration to total deaths and deaths from CVD, ischemic heart disease (IHD) and acute myocardial infarction (AMI) among 11,261 adults (>30 years at baseline) in the NHANES Follow-up Study. Participants were followed through December 31, 2006. We used Cox proportional hazard regression and adjusted for a variety of confounders, including sex, age, race, current and former tobacco use, income, obesity, diet, cholesterol, urinary cadmium, diabetes, hypertension, and physical activity. Results: During a median follow-up of 14.5 years, 3,290 participants died; 1,430 (43%) from CVD. The geometric mean blood lead of participants at baseline was 2.95 mg/dL; 3,065 (21.7%) had a blood lead >5 mg/dL. Adjusted hazard ratios for death among participants with blood lead levels in the highest tertile (> 3.9 mg/dL) compared with the lowest tertile (< 1.9 mg/dL) were 1.28 (95%CI=1.10, 1.49) for total mortality, 1.56 (95%CI=1.20, 2.01) for CVD mortality, 1.71 (95%CI=1.19, 2.45) for IHD mortality and 1.97 (95%CI=1.15, 3.36) for AMI mortality. Adjusted population attributable fractions were 11.2% (95% CI=0.13, 21) for total mortality, 19.2% (95%CI=4.4 to 32) for CVD mortality, 23.3% for IHD mortality (95%CI=5.3 to 38), and 27.1% (95%CI=5.7 to 43.6) for AMI mortality. We estimate that lead exposure accounts for 247,000 avoidable deaths each year; 175,000 from CVD, 122,000 from IHD deaths and 55,000 from AMI. Conclusion: Low-level, environmental lead exposure is a leading, but largely ignored risk factor for cardiovascular mortality in the United States.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".